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Field
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neural networks, covering representation, optimisation, generalisation, robustness and reliability, while remaining sufficiently tractable to inform engineering practice. A key objective is to transform
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needed for wet-bulb temperature retrieval -Co-locating satellite data with ground-based HadISD stations -Running neural networks and finding the architecture best suited to a case study on India
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Job Description The Halassa lab (https://halassalab.vtc.vt.edu/ ) invites applications for a Postdoctoral Associate position at the Fralin Biomedical Research Institute (FBRI) at Virginia Tech
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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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by connectome-constrained artificial neural networks. Candidates from outside the field of neuroscience are encouraged to apply, but must be curious, persistent, and passionate to delve
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. Key Responsibilities Research & Development: Integrate Physics-Informed Neural Networks or Reinforcement Learning to create realistic human movement and interactive social behaviors within XR
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neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications. A strong background in theory (e.g
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such as transformers, self-supervised learning, multimodal learning, generative models, graph neural networks, or foundation models. Experience with structural and/or functional brain modeling. Familiarity
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. Simulation-based inference (SBI) addresses this by training neural networks, such as flow-matching generative models, on simulated events. The project aims to develop efficient, robust and calibrated SBI